Decoding Chaos: Geospatial Data Exploratory Analysis (GDEA)
Note: This is a continuation of the take-home exercise 4. For Project Brief, Project Objective, Storyboard and Initial Exploratory Data Analysis, please view it under Initial Data Exploratory Analysis segment.
This project is separated into two segments:
The project uses p_load() of pacman package to check if the R packages are installed in the computer.
The following code chunk is used to install and launch the R packages.
pacman::p_load(tidyverse, kableExtra,
leaflet, rmarkdown, knitr,
highcharter, # timeseries highchart
viridis, ggthemes,
ggplot2, tidyr, dplyr, viridisLite, RColorBrewer,
countrycode, sf, spdep, tmap, leaflet, # geospatial
leaflet.extras, magrittr, mapview, leafsync, # hotspot
tm, plotly)final <- readRDS("data/final.rds")coords argument requires you to provide the column name of the x-coordinates first then followed by the column name of the y-coordinates.
crs argument requires you to provide the coordinates system in epsg format.
EPSG:4979 is WGS 84 Geographic Coordinate system and EPSG:4144 is Myanmar Projected Coordinate System.
Use st_as_sf() from sf package to convert listing data frame into simple feature data frame.
data_sf <- st_as_sf(final,
coords = c("longitude", "latitude"),
crs=4979) %>%
st_transform(crs = 4144)write_rds(data_sf,
"data/myan.rds")glimpse(data_sf)Rows: 13,177
Columns: 29
$ event_id_cnty <chr> "MMR56370", "MMR56871", "MMR56878", "MMR56900", "…
$ event_date <date> 2023-12-31, 2023-12-31, 2023-12-31, 2023-12-31, …
$ year <int> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2…
$ disorder_type <chr> "Political violence", "Political violence", "Poli…
$ event_type <chr> "Battles", "Battles", "Explosions/Remote violence…
$ sub_event_type <chr> "Armed clash", "Armed clash", "Air/drone strike",…
$ actor1 <chr> "Phoenix DF: Phoenix Defense Force (Nattalin)", "…
$ inter1 <int> 3, 3, 1, 3, 3, 1, 3, 1, 2, 3, 1, 2, 3, 2, 1, 1, 3…
$ actor2 <chr> "Military Forces of Myanmar (2021-)", "Military F…
$ inter2 <int> 1, 1, 7, 1, 1, 7, 1, 7, 1, 1, 3, 1, 1, 1, 7, 7, 3…
$ interaction <int> 13, 13, 17, 13, 13, 17, 13, 17, 12, 13, 13, 12, 1…
$ civilian_targeting <chr> "", "", "Civilian targeting", "", "", "Civilian t…
$ iso <int> 104, 104, 104, 104, 104, 104, 104, 104, 104, 104,…
$ region <chr> "Southeast Asia", "Southeast Asia", "Southeast As…
$ country <chr> "Myanmar", "Myanmar", "Myanmar", "Myanmar", "Myan…
$ admin1 <chr> "Bago-West", "Mon", "Sagaing", "Sagaing", "Mandal…
$ admin2 <chr> "Thayarwady", "Mawlamyine", "Katha", "Monywa", "M…
$ admin3 <chr> "Nattalin", "Ye", "Tigyaing", "Chaung-U", "Taungt…
$ location <chr> "Kyauk Pyoke", "Kyaung Ywar", "Kan Pauk", "Chaung…
$ source <chr> "Khit Thit Media; Myanmar Pressphoto Agency", "De…
$ notes <chr> "On 31 December 2023, near Kyauk Pyoke village (N…
$ fatalities <int> 4, 3, 1, 1, 2, 1, 3, 1, 1, 4, 2, 4, 2, 1, 1, 7, 3…
$ total_fata <int> 15716, 15716, 15716, 15716, 15716, 15716, 15716, …
$ total_inci <int> 4054, 4054, 4054, 4054, 4054, 4054, 4054, 4054, 4…
$ political_rate <dbl> 99.46, 99.46, 99.46, 99.46, 99.46, 99.46, 99.46, …
$ civilian_rate <dbl> 23.33, 23.33, 23.33, 23.33, 23.33, 23.33, 23.33, …
$ non_state_exchange <dbl> 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0…
$ govt_regain_exchange <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ geometry <POINT [°]> POINT (95.8295 18.6245), POINT (97.98861 15…Use st_transform() to re-project armed conflict incident points from one coordinate system to another coordinate system mathematically.
data_sf4144 <- st_transform(data_sf, 4144)st_crs(data_sf4144)Coordinate Reference System:
User input: EPSG:4144
wkt:
GEOGCRS["Kalianpur 1937",
DATUM["Kalianpur 1937",
ELLIPSOID["Everest 1830 (1937 Adjustment)",6377276.345,300.8017,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Geodesy."],
AREA["Bangladesh - onshore; India - mainland onshore; Myanmar - onshore and Moattama area offshore; Pakistan - onshore."],
BBOX[8.02,60.86,37.07,101.17]],
ID["EPSG",4144]]Myanmar Level-3 Administrative Boundary (also known as Local Government Area, LGA) polygon features GIS data was downloaded from geoBoundaries.
Use st_read to read geospatial layers. Use st_union to combine three
# myan_bound_adm1 <- st_read(dsn="data", layer="geoBoundaries-MMR-ADM1")
myan_bound_adm2 <- st_read(dsn="data",
layer="geoBoundaries-MMR-ADM2")Reading layer `geoBoundaries-MMR-ADM2' from data source
`C:\teoose\ISSS608\Take-home_Ex\Take-home_Ex04\data' using driver `ESRI Shapefile'
Simple feature collection with 74 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 92.17275 ymin: 9.671252 xmax: 101.1699 ymax: 28.54554
Geodetic CRS: WGS 84# myan_bound_adm3 <- st_read(dsn="data", layer="geoBoundaries-MMR-ADM3")myan_bound <- myan_bound_adm2glimpse(myan_bound)Rows: 74
Columns: 6
$ shapeName <chr> "Hinthada", "Labutta", "Maubin", "Myaungmya", "Pathein", "P…
$ shapeISO <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ shapeID <chr> "60261553B48695999525720", "60261553B3473904040713", "60261…
$ shapeGroup <chr> "MMR", "MMR", "MMR", "MMR", "MMR", "MMR", "MMR", "MMR", "MM…
$ shapeType <chr> "ADM2", "ADM2", "ADM2", "ADM2", "ADM2", "ADM2", "ADM2", "AD…
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((95.2637 18...., MULTIPOLYGON (…write_rds(myan_bound,
"data/pt_myan.rds")Use st_crs() to check the set coordinate system of myan_bound.
st_crs(myan_bound)Coordinate Reference System:
User input: WGS 84
wkt:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]Use st_transform() to assign correct EPSG code to dataframe, from one coordinate system to another coordinate system mathematically.
myan_bound4144 <- st_transform(myan_bound, 4144)st_crs(myan_bound4144)Coordinate Reference System:
User input: EPSG:4144
wkt:
GEOGCRS["Kalianpur 1937",
DATUM["Kalianpur 1937",
ELLIPSOID["Everest 1830 (1937 Adjustment)",6377276.345,300.8017,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Geodesy."],
AREA["Bangladesh - onshore; India - mainland onshore; Myanmar - onshore and Moattama area offshore; Pakistan - onshore."],
BBOX[8.02,60.86,37.07,101.17]],
ID["EPSG",4144]]Use st_join to create new variable (based on year) Use tapply to
To find out the number of fatalities and armed conflict incidents in each third-level administrative region.
We will extract the fatalities records by event_type field.
for (year in 2010:2023) {
# Filter data_sf4144 for the current year
data_sf4144_year <- data_sf4144[data_sf4144$year == year, ]
# Perform spatial join
joined_data <- st_join(myan_bound4144, data_sf4144_year)
##############
# Summarise total fatalities at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no fatalities
total_fata_col <- paste0('total_fata_', year)
myan_bound4144[[total_fata_col]] <- tapply(joined_data$fatalities, joined_data$shapeID, sum)
myan_bound4144[[total_fata_col]][is.na(myan_bound4144[[total_fata_col]])] <- 0
# Summarise percentage of fatalities at each adm2 level in myan_bound4144
pct_fata_col <- paste0('pct_fata_', year)
myan_bound4144[[pct_fata_col]] <- round(myan_bound4144[[total_fata_col]] / sum(myan_bound4144[[total_fata_col]]) * 100, 2)
myan_bound4144[[pct_fata_col]][is.na(myan_bound4144[[pct_fata_col]])] <- 0
##############
# Summarise total incidents at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no incidents
total_inci_col <- paste0('total_inci_', year)
myan_bound4144[[total_inci_col]] <- tapply(joined_data$year, joined_data$shapeID, sum)
myan_bound4144[[total_inci_col]][is.na(myan_bound4144[[total_inci_col]])] <- 0
# Summarise percentage of incidents at each adm2 level in myan_bound4144
pct_inci_col <- paste0('pct_fata_', year)
myan_bound4144[[pct_inci_col]] <- round(myan_bound4144[[total_inci_col]] / sum(myan_bound4144[[total_inci_col]]) * 100, 2)
myan_bound4144[[pct_inci_col]][is.na(myan_bound4144[[pct_inci_col]])] <- 0
##############
# Summarise total political violence rate at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no incidents
total_politicalrate_col <- paste0('total_politicalrate_', year)
myan_bound4144[[total_politicalrate_col]] <- tapply(joined_data$political_rate, joined_data$shapeID, sum)
myan_bound4144[[total_politicalrate_col]][is.na(myan_bound4144[[total_politicalrate_col]])] <- 0
# Summarise percentage of political violence rate at each adm2 level in myan_bound4144
pct_politicalrate_col <- paste0('pct_politicalrate_', year)
myan_bound4144[[pct_politicalrate_col]] <-
round(myan_bound4144[[total_politicalrate_col]] / sum(myan_bound4144[[total_inci_col]]) * 100, 2)
myan_bound4144[[pct_politicalrate_col]][is.na(myan_bound4144[[pct_politicalrate_col]])] <- 0
##############
# Summarise total civilian violence rate at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no incidents
total_civilianrate_col <- paste0('total_civilianrate_', year)
myan_bound4144[[total_civilianrate_col]] <- tapply(joined_data$civilian_rate, joined_data$shapeID, sum)
myan_bound4144[[total_civilianrate_col]][is.na(myan_bound4144[[total_civilianrate_col]])] <- 0
# Summarise percentage of civilian violence rate at each adm2 level in myan_bound4144
pct_civilianrate_col <- paste0('pct_civilianrate_', year)
myan_bound4144[[pct_civilianrate_col]] <-
round(myan_bound4144[[total_civilianrate_col]] / sum(myan_bound4144[[total_inci_col]]) * 100, 2)
myan_bound4144[[pct_civilianrate_col]][is.na(myan_bound4144[[pct_civilianrate_col]])] <- 0
##############
# Summarise non-state ex rate at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no incidents
total_nonstateex_col <- paste0('total_nonstateex_', year)
myan_bound4144[[total_nonstateex_col]] <- tapply(joined_data$non_state_exchange, joined_data$shapeID, sum)
myan_bound4144[[total_nonstateex_col]][is.na(myan_bound4144[[total_civilianrate_col]])] <- 0
# Summarise percentage of non-state ex rate at each adm2 level in myan_bound4144
pct_nonstateex_col <- paste0('pct_nonstateex_', year)
myan_bound4144[[pct_nonstateex_col]] <-
round(myan_bound4144[[total_nonstateex_col]] / sum(myan_bound4144[[total_inci_col]]) * 1000)
myan_bound4144[[pct_nonstateex_col]][is.na(myan_bound4144[[pct_nonstateex_col]])] <- 0
##############
# Summarise govt regain rate at each adm2 level in myan_bound4144
# Replace NA with 0 for polygons with no incidents
total_govtregain_col <- paste0('total_govtregain_', year)
myan_bound4144[[total_govtregain_col]] <- tapply(joined_data$govt_regain_exchange, joined_data$shapeID, sum)
myan_bound4144[[total_govtregain_col]][is.na(myan_bound4144[[total_govtregain_col]])] <- 0
# Summarise percentage of govt regain rate at each adm2 level in myan_bound4144
pct_govtregain_col <- paste0('pct_govtregain_', year)
myan_bound4144[[pct_govtregain_col]] <-
round(myan_bound4144[[total_govtregain_col]] / sum(myan_bound4144[[total_inci_col]]) * 1000)
myan_bound4144[[pct_govtregain_col]][is.na(myan_bound4144[[pct_govtregain_col]])] <- 0
##############
# Summarise by event_type at each adm2 level in myan_bound4144
event_types <- unique(data_sf4144$event_type)
for (event_type in event_types) {
event_count_col <- paste0(event_type, '_', year)
myan_bound4144[[event_count_col]] <- tapply(joined_data$event_type == event_type, joined_data$shapeID, sum)
myan_bound4144[[event_count_col]][is.na(myan_bound4144[[event_count_col]])] <- 0
}
}Remove duplicated rows
# remove duplicated rows
duplicate_rows <- duplicated(myan_bound4144)
myan_bound4144 <- myan_bound4144[!duplicate_rows, ]Use st_intersect() to identify armed conflicts located inside each administrative region.
myan_bound4144$total_inci <-
lengths(st_intersects(myan_bound4144, data_sf4144))Calculate total fatalities in each administrative region.
myan_bound4144$total_fata <-
tapply(joined_data$fatalities, joined_data$shapeID, sum)summary(myan_bound4144$total_inci) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 42.0 113.5 178.0 281.8 845.0 Use top_n() to list the second third-level administrative region of incident counts.
top_n(myan_bound4144, 1, total_inci) %>%
kable()| shapeName | shapeISO | shapeID | shapeGroup | shapeType | geometry | total_fata_2010 | pct_fata_2010 | total_inci_2010 | total_politicalrate_2010 | pct_politicalrate_2010 | total_civilianrate_2010 | pct_civilianrate_2010 | total_nonstateex_2010 | pct_nonstateex_2010 | total_govtregain_2010 | pct_govtregain_2010 | Battles_2010 | Explosions/Remote violence_2010 | Violence against civilians_2010 | Protests_2010 | Strategic developments_2010 | Riots_2010 | total_fata_2011 | pct_fata_2011 | total_inci_2011 | total_politicalrate_2011 | pct_politicalrate_2011 | total_civilianrate_2011 | pct_civilianrate_2011 | total_nonstateex_2011 | pct_nonstateex_2011 | total_govtregain_2011 | pct_govtregain_2011 | Battles_2011 | Explosions/Remote violence_2011 | Violence against civilians_2011 | Protests_2011 | Strategic developments_2011 | Riots_2011 | total_fata_2012 | pct_fata_2012 | total_inci_2012 | total_politicalrate_2012 | pct_politicalrate_2012 | total_civilianrate_2012 | pct_civilianrate_2012 | total_nonstateex_2012 | pct_nonstateex_2012 | total_govtregain_2012 | pct_govtregain_2012 | Battles_2012 | Explosions/Remote violence_2012 | Violence against civilians_2012 | Protests_2012 | Strategic developments_2012 | Riots_2012 | total_fata_2013 | pct_fata_2013 | total_inci_2013 | total_politicalrate_2013 | pct_politicalrate_2013 | total_civilianrate_2013 | pct_civilianrate_2013 | total_nonstateex_2013 | pct_nonstateex_2013 | total_govtregain_2013 | pct_govtregain_2013 | Battles_2013 | Explosions/Remote violence_2013 | Violence against civilians_2013 | Protests_2013 | Strategic developments_2013 | Riots_2013 | total_fata_2014 | pct_fata_2014 | total_inci_2014 | total_politicalrate_2014 | pct_politicalrate_2014 | total_civilianrate_2014 | pct_civilianrate_2014 | total_nonstateex_2014 | pct_nonstateex_2014 | total_govtregain_2014 | pct_govtregain_2014 | Battles_2014 | Explosions/Remote violence_2014 | Violence against civilians_2014 | Protests_2014 | Strategic developments_2014 | Riots_2014 | total_fata_2015 | pct_fata_2015 | total_inci_2015 | total_politicalrate_2015 | pct_politicalrate_2015 | total_civilianrate_2015 | pct_civilianrate_2015 | total_nonstateex_2015 | pct_nonstateex_2015 | total_govtregain_2015 | pct_govtregain_2015 | Battles_2015 | Explosions/Remote violence_2015 | Violence against civilians_2015 | Protests_2015 | Strategic developments_2015 | Riots_2015 | total_fata_2016 | pct_fata_2016 | total_inci_2016 | total_politicalrate_2016 | pct_politicalrate_2016 | total_civilianrate_2016 | pct_civilianrate_2016 | total_nonstateex_2016 | pct_nonstateex_2016 | total_govtregain_2016 | pct_govtregain_2016 | Battles_2016 | Explosions/Remote violence_2016 | Violence against civilians_2016 | Protests_2016 | Strategic developments_2016 | Riots_2016 | total_fata_2017 | pct_fata_2017 | total_inci_2017 | total_politicalrate_2017 | pct_politicalrate_2017 | total_civilianrate_2017 | pct_civilianrate_2017 | total_nonstateex_2017 | pct_nonstateex_2017 | total_govtregain_2017 | pct_govtregain_2017 | Battles_2017 | Explosions/Remote violence_2017 | Violence against civilians_2017 | Protests_2017 | Strategic developments_2017 | Riots_2017 | total_fata_2018 | pct_fata_2018 | total_inci_2018 | total_politicalrate_2018 | pct_politicalrate_2018 | total_civilianrate_2018 | pct_civilianrate_2018 | total_nonstateex_2018 | pct_nonstateex_2018 | total_govtregain_2018 | pct_govtregain_2018 | Battles_2018 | Explosions/Remote violence_2018 | Violence against civilians_2018 | Protests_2018 | Strategic developments_2018 | Riots_2018 | total_fata_2019 | pct_fata_2019 | total_inci_2019 | total_politicalrate_2019 | pct_politicalrate_2019 | total_civilianrate_2019 | pct_civilianrate_2019 | total_nonstateex_2019 | pct_nonstateex_2019 | total_govtregain_2019 | pct_govtregain_2019 | Battles_2019 | Explosions/Remote violence_2019 | Violence against civilians_2019 | Protests_2019 | Strategic developments_2019 | Riots_2019 | total_fata_2020 | pct_fata_2020 | total_inci_2020 | total_politicalrate_2020 | pct_politicalrate_2020 | total_civilianrate_2020 | pct_civilianrate_2020 | total_nonstateex_2020 | pct_nonstateex_2020 | total_govtregain_2020 | pct_govtregain_2020 | Battles_2020 | Explosions/Remote violence_2020 | Violence against civilians_2020 | Protests_2020 | Strategic developments_2020 | Riots_2020 | total_fata_2021 | pct_fata_2021 | total_inci_2021 | total_politicalrate_2021 | pct_politicalrate_2021 | total_civilianrate_2021 | pct_civilianrate_2021 | total_nonstateex_2021 | pct_nonstateex_2021 | total_govtregain_2021 | pct_govtregain_2021 | Battles_2021 | Explosions/Remote violence_2021 | Violence against civilians_2021 | Protests_2021 | Strategic developments_2021 | Riots_2021 | total_fata_2022 | pct_fata_2022 | total_inci_2022 | total_politicalrate_2022 | pct_politicalrate_2022 | total_civilianrate_2022 | pct_civilianrate_2022 | total_nonstateex_2022 | pct_nonstateex_2022 | total_govtregain_2022 | pct_govtregain_2022 | Battles_2022 | Explosions/Remote violence_2022 | Violence against civilians_2022 | Protests_2022 | Strategic developments_2022 | Riots_2022 | total_fata_2023 | pct_fata_2023 | total_inci_2023 | total_politicalrate_2023 | pct_politicalrate_2023 | total_civilianrate_2023 | pct_civilianrate_2023 | total_nonstateex_2023 | pct_nonstateex_2023 | total_govtregain_2023 | pct_govtregain_2023 | Battles_2023 | Explosions/Remote violence_2023 | Violence against civilians_2023 | Protests_2023 | Strategic developments_2023 | Riots_2023 | total_inci | total_fata |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Shwebo | NA | 60261553B74508542584232 | MMR | ADM2 | MULTIPOLYGON (((95.96527 22... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 0.72 | 40420 | 1963 | 0.04 | 832.4 | 0.01 | 0.8 | 0 | 0 | 0 | 5 | 7 | 7 | 1 | 0 | 0 | 77 | 0.54 | 48528 | 2384.4 | 0.03 | 632.64 | 0.01 | 6 | 0 | 2.4 | 0 | 9 | 4 | 11 | 0 | 0 | 0 | 14 | 0.27 | 22253 | 1094.06 | 0.01 | 256.63 | 0 | 0.22 | 0 | 0 | 0 | 1 | 1 | 9 | 0 | 0 | 0 | 845 | 14 |
Use st_area() to derive the area of each second-level administrative region.
myan_bound4144$Area <- myan_bound4144 %>%
st_area()Use code chunk below to compute density area.
mapping_rates <- myan_bound4144 %>%
mutate(# density rates
inci_density = as.numeric(`total_inci`/Area * 1000000),
fata_density = as.numeric(`total_fata`/Area * 1000000)) %>%
replace_na(list(inci_density = 0, fata_density = 0, total_fata = 0, total_inci = 0)) %>%
select(shapeName, shapeID, shapeGroup, shapeType, geometry, total_inci, inci_density,
total_fata, fata_density, starts_with("pct"))tmap_mode("plot")https://spatialanalysis.github.io/lab_tutorials/4_R_Mapping.html
Use Box map
Create boxbreak functions
The code chunk below is an R function that creating break points for a box map.
arguments: v: vector with observations mult: multiplier for IQR (default 1.5) returns: bb: vector with 7 break points compute quartile and fences
boxbreaks <- function(v,mult=1.5) {
qv <- unname(quantile(v))
iqr <- qv[4] - qv[2]
upfence <- qv[4] + mult * iqr
lofence <- qv[2] - mult * iqr
# initialize break points vector
bb <- vector(mode="numeric",length=7)
# logic for lower and upper fences
if (lofence < qv[1]) { # no lower outliers
bb[1] <- lofence
bb[2] <- floor(qv[1])
} else {
bb[2] <- lofence
bb[1] <- qv[1]
}
if (upfence > qv[5]) { # no upper outliers
bb[7] <- upfence
bb[6] <- ceiling(qv[5])
} else {
bb[6] <- upfence
bb[7] <- qv[5]
}
bb[3:5] <- qv[2:4]
return(bb)
}Creating the get.var function The code chunk below is an R function to extract a variable as a vector out of an sf data frame.
arguments: vname: variable name (as character, in quotes) df: name of sf data frame returns: v: vector with values (without a column name)
get.var <- function(vname,df) {
v <- df[vname] %>% st_set_geometry(NULL)
v <- unname(v[,1])
return(v)
}Boxmap function The code chunk below is an R function to create a box map. arguments:
vnam: variable name (as character, in quotes)
df: simple features polygon layer
legtitle: legend title
mtitle: map title
mult: multiplier for IQR
returns: a tmap-element (plots a map)
boxmap <- function(vnam, df,
legtitle=NA,
mtitle= paste0("Box Map of ", vnam),
mult=1.5){
var <- get.var(vnam,df)
bb <- boxbreaks(var)
tm_shape(df) +
tm_polygons() +
tm_shape(df) +
tm_fill(vnam,title=legtitle,
breaks=bb,
palette="Reds",
labels = c("lower outlier",
"< 25%",
"25% - 50%",
"50% - 75%",
"> 75%",
"upper outlier")) +
tm_borders() +
tm_layout(main.title = mtitle,
title.position = c("left",
"top"))
}box_inci <- boxmap("total_inci", mapping_rates)
box_fata <- boxmap("total_fata", mapping_rates)tmap_arrange(box_fata, box_inci, asp=1, ncol=2)
Density map
To visualise spatial density (concentration) within the different geographical subnational administrative regions.
kmeans Classification
inci_den <- tm_shape(mapping_rates) +
tm_fill("inci_density",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Density of Armed Conflicts in Myanmar \n(kmeans Classification)",
title = "",
main.title.size = 1,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
fata_den <- tm_shape(mapping_rates) +
tm_fill("fata_density",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Density of Fatalities in Myanmar \n(kmeans Classification)",
title = "",
main.title.size = 1,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))tmap_arrange(inci_den, fata_den,
asp = 1,
ncol=2, nrow=1)
Choropleth Map ::: {.callout-note icon=“false”} ### Design Features - XXX
To visualise spatial pattern or distribution across different geographical subnational administrative regions.
class_equal <- tm_shape(mapping_rates) +
tm_fill("total_inci",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of Armed Conflicts in Myanmar \nEqual classification",
title = "(2010-2023)",
main.title.size = 0.7,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
class_pretty <- tm_shape(mapping_rates) +
tm_fill("total_inci",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of Armed Conflicts in Myanmar \nPretty classification",
title = "(2010-2023)",
main.title.size = 0.7,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
class_quant <- tm_shape(myan_bound4144) +
tm_fill("total_inci",
n = 10,
style = "quantile",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of Armed Conflicts in Myanmar \nQuantile classification",
title = "(2010-2023)",
main.title.size = 0.7,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
class_kmeans <- tm_shape(myan_bound4144) +
tm_fill("total_inci",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of Armed Conflicts in Myanmar \nKmeans classification",
title = "(2010-2023)",
main.title.size = 0.7,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))tmap_arrange(class_equal, class_pretty, class_quant, class_kmeans, asp=1, ncol=4) 
kmeans Classification
pv10 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2010",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2010",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv11 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2011",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2011",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv12 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2012",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2012",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv13 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2013",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2013",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv14 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2014",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2014",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv15 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2015",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2015",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv16 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2016",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2016",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv17 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2017",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2017",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv18 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2018",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2018",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv19 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2019",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2019",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv20 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2020",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2020",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv21 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2021",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2021",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv22 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2022",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2022",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
pv23 <- tm_shape(mapping_rates) +
tm_fill("pct_politicalrate_2023",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Political Violence \nin Myanmar (kmeans Classification)",
title = "2023",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))tmap_arrange(pv10, pv11, pv12, pv13, pv14, pv15, pv16,
pv17, pv18, pv19, pv20, pv21, pv22, pv23,
asp = 1,
ncol=7, nrow=2)
kmeans Classification
cv10 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2010",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2010",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv11 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2011",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2011",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv12 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2012",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2012",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv13 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2013",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2013",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv14 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2014",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2014",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv15 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2015",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2015",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv16 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2016",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2016",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv17 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2017",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2017",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv18 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2018",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2018",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv19 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2019",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2019",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv20 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2020",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2020",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv21 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2021",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2021",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv22 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2022",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2022",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
cv23 <- tm_shape(mapping_rates) +
tm_fill("pct_civilianrate_2023",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Violence against Civilians \nin Myanmar (kmeans Classification)",
title = "2023",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))tmap_arrange(cv10, cv11, cv12, cv13, cv14, cv15, cv16,
cv17, cv18, cv19, cv20, cv21, cv22, cv23,
asp = 1,
ncol=7, nrow=2)
equal Classification
gr10 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2010",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2010",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr11 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2011",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2011",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr12 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2012",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2012",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr13 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2013",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2013",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr14 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2014",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2014",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr15 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2015",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2015",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr16 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2016",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2016",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr17 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2017",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2017",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr18 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2018",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2018",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr19 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2019",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2019",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr20 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2020",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2020",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr21 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2021",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2021",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr22 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2022",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2022",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
gr23 <- tm_shape(mapping_rates) +
tm_fill("pct_govtregain_2023",
n = 10,
style = "equal",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Government Regaining \nin Myanmar (equal Classification)",
title = "2023",
main.title.size = 0.8,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))2015 & 2017
tmap_arrange(gr15, gr17, asp = 1,
ncol=2, nrow=1)
to remove??? pretty Classification
nse10 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2010",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2010",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse11 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2011",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2011",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse12 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2012",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2012",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse13 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2013",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2013",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse14 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2014",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2014",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse15 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2015",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2015",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse16 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2016",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2016",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse17 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2017",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2017",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse18 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2018",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2018",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse19 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2019",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2019",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse20 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2020",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2020",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse21 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2021",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2021",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse22 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2022",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2022",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
nse23 <- tm_shape(mapping_rates) +
tm_fill("pct_nonstateex_2023",
n = 10,
style = "pretty",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Non-state Overtaking \nin Myanmar (equal Classification)",
title = "2023",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))kmeans Classification
f10 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2010",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2010",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f11 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2011",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2011",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f12 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2012",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2012",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f13 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2013",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2013",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f14 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2014",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2014",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f15 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2015",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2015",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f16 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2016",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2016",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f17 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2017",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2017",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f18 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2018",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2018",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f19 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2019",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2019",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f20 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2020",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2020",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f21 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2021",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2015",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f22 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2022",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2022",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
f23 <- tm_shape(mapping_rates) +
tm_fill("pct_fata_2023",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Spatial Distribution of Fatalities \nin Myanmar (kmeans Classification)",
title = "2023",
main.title.size = 0.55,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))tmap_arrange(f10, f11, f12, f13, f14, f15, f16,
f17, f18, f19, f20, f21, f22, f23,
asp = 1,
ncol=7, nrow=2)
Interactive Choropleth Map
tmap_obj <- tm_shape(mapping_rates) +
tm_fill("total_inci",
n = 10,
style = "kmeans",
palette = "Reds",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_borders(lwd = 0.1, alpha = 1) +
tm_layout(main.title = "Distribution of Armed Conflict Incidents in Myanmar \nKmeans classification",
title = "(2010-2023)",
main.title.size = 1,
legend.height = 0.60,
legend.width = 5.0,
legend.outside = FALSE,
legend.position = c("left", "bottom"))
leaflet_map <- tmap_leaflet(tmap_obj) %>%
addTiles('https://basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
addPolygons(data = mapping_rates,
label = ~paste("Custom Tooltip Content: ", mapping_rates$total_inci, "\n", mapping_rates$total_fata))
leaflet_mapPoint Symbol Map
To show the specific armed conflict events based on their geographical coordinates. Use of long and lat.
cof <- colorFactor(c("#ff7e8a", "#394938", "#ffa500", "#0092ff", "#741b47", "#60dcb5"),
domain=c("Battles", "Explosions/ Remote violence", "Protests", "Riots",
"Strategic developments", "Violence against civilians"))
incident_pts <- final %>%
filter(fatalities > 0)
psmap <- leaflet() %>%
addTiles('https://basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
setView(100, 20, zoom = 5) %>%
addCircles(data=incident_pts, lat= ~latitude, lng = ~longitude, color = ~cof(event_type),
fillColor = "black",
popup = paste( "<strong>", incident_pts$event_date, "</strong>",
"<br><strong>Country: </strong>",
incident_pts$country,
"<br><strong>Sub-national Admin Region: </strong>",
incident_pts$admin1, "/",
incident_pts$admin2, "/",
incident_pts$admin3,
"<br><strong>Event type: </strong>",
incident_pts$event_type,
"<br><strong>Sub-event type: </strong>",
incident_pts$sub_event_type,
"<br><strong>Summary: </strong>",
incident_pts$notes,
"<br><strong>Total Fatalities: </strong>",
incident_pts$fatalities)) %>%
addLegend("bottomright", colors = c("#ff7e8a", "#394938", "#ffa500", "#0092ff", "#741b47", "#60dcb5"),
labels = c("Battles", "Explosions/ Remote violence", "Protests", "Riots",
"Strategic developments", "Violence against civilians"), title="Event Type")
psmapUse Hotspot map
never use: https://trucvietle.me/r/tutorial/2017/01/18/spatial-heat-map-plotting-using-r.html
https://www.supplychaindataanalytics.com/leaflet-heatmaps-in-r/
incident_pts <- final %>%
filter(fatalities > 0)
# create fatalities hotspot intensity
hotmap_fata <- leaflet() %>%
addTiles('https://basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
setView(100, 20, zoom = 5) %>%
addHeatmap(data=incident_pts, lng = ~longitude, lat = ~latitude, intensity = ~total_fata, max = 100, radius = 10, blur = 10)
incident_pts <- final %>%
filter(fatalities > 0)
# create armed conflict incidents hotspot intensity
hotmap_inci <- leaflet() %>%
addTiles('https://basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png') %>%
setView(100, 20, zoom = 5) %>%
addHeatmap(data=incident_pts, lng = ~longitude, lat = ~latitude, intensity = ~total_inci, max = 100, radius = 10, blur = 10) %>%
addPopups(data = incident_pts, lng = ~longitude, lat = ~latitude,
popup = ~paste("<strong>Date: </strong>", event_date, "<br><strong>Country: </strong>", country))
# latticeview(hotmap_fata, hotmap_inci)
hotmap_fata# switch tmap off
tmap_mode("plot")